Polymorphic codec system and method

ABSTRACT

An input module obtains a media signal to be communicated to a destination system, after which an identification module identifies a plurality of segments within the media signal. A codec includes a selection module that automatically selects different compression methods to respectively compress at least two of the segments. The compression methods are automatically selected to produce a highest compression quality for the respective segments according to a set of criteria without exceeding a target data rate. A compression module within the codec then compresses the segments using the automatically-selected compression methods, after which an output module delivers the compressed segments to the destination system with an indication of which compression method was used to compress each segment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/784,754, filed Feb. 23, 2004, which is a continuation-in-part of U.S.patent application Ser. No. 10/256,866, filed Sep. 26, 2002, whichclaims the benefit of Provisional Application No. 60/325,483, filed Sep.26, 2001. U.S. patent application Ser. No. 10/784,754 is also acontinuation-in-part of U.S. patent application Ser. No. 10/692,106,filed Oct. 23, 2003. All of the foregoing related applications areincorporated herein by reference.

TECHNICAL FIELD

The present invention relates generally to the field of datacompression. More specifically, the present invention relates totechniques for optimizing data compression for video communication.

BACKGROUND OF THE INVENTION

Conventionally, a codec uses a single type of algorithm to compressvideos signals. For example, many codecs, such as MPEG, use discretecosine transfer (DCT) algorithms, while others use fractal or waveletalgorithms. In some cases, a user may be able to select a particularcodec, but once the choice is made, the selected codec is usedthroughout a communication session.

Certain algorithms result in better compression and/or transmissionquality than others for media signals having particular characteristics.Unfortunately, the characteristics of a given media signal may varysubstantially during a transmission. Thus, using a single codec tocompress a media signal will often produce less than optimal results.

No existing system currently allows a single codec to use multiplecompression algorithms, such as DCT, fractal, wavelet, or otheralgorithms, within the same transmission.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a conventional communication system using acodec for data compression;

FIG. 2 is a block diagram of a communication system using a polymorphiccodec according to an embodiment of the invention;

FIG. 3 is a detailed block diagram of a source system according to afirst embodiment of the invention;

FIG. 4 is a detailed block diagram of a source system according to asecond embodiment of the invention;

FIG. 5 is a detailed block diagram of a selection module;

FIG. 6 is a data flow diagram of a process for automatically selecting acompression method within a polymorphic codec;

FIG. 7 is a detailed block diagram of an artificial intelligence systemfor selecting a compression method;

FIG. 8 is a table used by a comparison module to select a compressionmethod based, in part, on licensing cost;

FIG. 9 is a block diagram of source system changing its target datarate; and

FIG. 10 is a data flow diagram of a process for automatically selectingdifferent compression methods for different sub-frames.

DETAILED DESCRIPTION

Reference is now made to the figures in which like reference numeralsrefer to like or similar elements. For clarity, the first digit of areference numeral indicates the figure number in which the correspondingelement is first used.

In the following description, numerous specific details of programming,software modules, user selections, network transactions, databasequeries, database structures, etc., are provided for a thoroughunderstanding of the embodiments of the invention. However, thoseskilled in the art will recognize that the invention can be practicedwithout one or more of the specific details, or with other methods,components, materials, etc.

In some cases, well-known structures, materials, or operations are notshown or described in detail in order to avoid obscuring aspects of theinvention. Furthermore, the described features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

FIG. 1 is a block diagram of a conventional system 100 for communicatingmedia signals, such as audio and video signals, from a source system 102to a destination system 104. The source and destination systems 102, 104may be variously embodied, for example, as personal computers (PCs),cable or satellite set-top boxes (STBs), dedicated video conferencingsystems, or video-enabled portable devices, such as personal digitalassistants (PDAs) or cellular telephones.

Within the source system 102, a video camera 106 or other devicecaptures an original media signal 108. A codec (compressor/decompressor)110 processes the original media signal 108 using a particularcompression method (algorithm) 111 to create a compressed media signal112. General classifications of compression methods 111 include discretecosine transform (DCT) methods, fractal methods, and wavelet methods.Those of skill in the art, however, will recognize that a wide varietyof compression methods may be used.

The compressed media signal 112 may be delivered to the destinationsystem 104 via a network 114, such as a local area network (LAN) or theInternet. Alternatively, the compressed media signal 112 may be writtento a storage medium, such as a CD, DVD, flash memory device, or thelike.

At the destination system 104, the same or a similar codec 110 processesthe compressed media signal 112 method received through the network 114using a corresponding decompression method 115 to generate adecompressed media signal 116. The destination system 104 then presentsthe decompressed media signal 116 on a display device 118, such as atelevision, computer monitor, or the like.

Conventionally, the codec 110 uses a single compression method 111 toprocess the entire media signal 108 during a communication session orfor a particular storage medium. However, as noted above, a media signalis not a static quantity. Video signals may change substantially fromscene to scene. A single compression method 111, which may function wellunder certain conditions, may not fare so well under differentconditions. Changes in available bandwidth, line conditions, orcharacteristics of the media signal, itself, may drastically change thecompression quality to the point that a different compression method 111may do much better.

In certain cases, a video engineer may be able to manually specify achange of codec 110 within a media signal 108 where, for instance, thecontent developer knows that one codec 110 may be superior to anothercodec 110. However, this requires significant human effort and cannot beperformed in real time.

FIG. 2 is a block diagram of a system 200 for communicating mediasignals from a source system 202 to a destination system 204 accordingto an embodiment of the present invention. As before, the source system202 receives an original media signal 108 captured by a video camera 106or other suitable device.

However, unlike the system 100 of FIG. 1, the depicted system 200 is notlimited to using a codec 110 with a single compression method 111.Rather, each scene 206 or segment of the original media signal 108 maybe compressed using one of a plurality of compression methods 111 of apolymorphic codec 208. As explained below, the polymorphic codec 208 iscapable of changing its form during a communication session to usepotentially different compression methods 111 for each scene 206.

A scene 206 may include one or more “frames” of the original mediasignal 108. A frame is generally defined as a single image in a sequenceof images. As used herein, a scene 206 may correspond to a fixed segmentof the media signal 108, e.g., two seconds of video or a fixed number offrames. In other embodiments, a scene 206 may be defined bycharacteristics of the original media signal 108, i.e., a scene 206 mayinclude two or more frames sharing similar characteristics.

As illustrated, four scenes 206 within the same media signal 108 may becompressed using four automatically-selected compression methods 111a-d. The compression methods 111 a-d may be of various types known tothose of skill in the art, e.g., DCT, fractal, wavelet, and the like.

Unlike conventional systems 100, the system 200 of FIG. 2 automaticallyselects, from the available compression methods 111, a particular method111 best suited to compressing each scene 206. Details of the selectionprocess are described in greater detail below. Briefly, however, thesystem 200 records which compression methods 111 are used for scenes 206having particular characteristics. If a subsequent scene 206 isdetermined to have the same characteristics, the same compression method111 is used. However, if a scene 206 is found to have substantiallydifferent characteristics from those previously observed, the system 200tests various compression methods 111 on the scene 206 and selects themethod 111 producing the highest compression quality (i.e., how similarthe compressed media signal 210 is to the original signal 108 afterdecompression) for a particular target data rate.

In addition, the source system 202 reports to the destination system 204which compression method 111 was used to compress each scene 206. Asillustrated, this may be accomplished by associating method identifiers209 with each scene 206 in the resulting compressed media signal 210.The method identifiers 209 may precede each scene 206, as shown, orcould be sent as a block at some point during the transmission. Theprecise format of the method identifiers 209 is not crucial to theinvention and may be implemented using standard data structures known tothose of skill in the art.

The destination system 204 uses the method identifiers 209 to select thecorresponding decompression methods 115 for decompressing the respectivescenes 206. The resulting decompressed media signal 116 may then bepresented on the display device 118, as previously described.

FIG. 3 illustrates additional details related to the source system 202.In one embodiment, an input module 302 receives the original mediasignal 108 from the video camera 106 or other source device. Anidentification module 304 divides the original media signal 108 intoscenes 206 and identifies various characteristics of each scene 206, asdescribed in greater detail below.

Thereafter, for each scene 206, a selection module 306 selects theoptimal compression method 111 for the scene 206 based on thecharacteristics or through a process of testing various compressionmethods 111. As used herein, “optimal” means producing the highestcompression quality for the compressed media signal 210 at a particulartarget data rate among the available compression methods 111 for thepolymorphic codec 208.

In one embodiment, a user may specify a particular target data rate,i.e., 128 kilobits per second (kbps), which may be selected, forinstance, from a menu or the like. Alternatively, the target data ratemay be automatically determined from the type of network 114, the typeof destination system 204, etc.

The polymorphic codec 208 may provide a wide variety of compressionmethods 111. Examples of possible compression methods 111 for video areprovided in Table 1. Additionally, various audio codecs may be provided,such as MPEG Audio Layer 3 (MP3), MPEG-4 Structured Audio (MP4-SA),CCITT u-Law, Ogg Vorbis, and AC3. Of course, other presently-availableor yet-to-be-developed compression methods may be used within the scopeof the invention.

TABLE 1 FOURCC Name Owner 3IV1 3ivx 3IVX 3IV2 3ivx 3IVX AASC AutodeskAnimator codec Autodesk ADV1 WaveCodec Loronix ADVJ Avid M-JPEG AvidTechnology AEMI Array VideoONE Array Microsystems MPEG 1-I Capture AFLIAutodesk Animator codec Autodesk AFLC Autodesk Animator codec AutodeskAMPG Array VideoONE MPEG Array Microsystems ANIM RDX Intel AP41AngelPotion Definitive AngelPotion ASV1 Asus Video Asus ASV2 Asus Video(2) Asus ASVX Asus Video 2.0 Asus AUR2 Aura 2 Codec - YUV 422 AuravisionAURA Aura 1 Codec - YUV 411 Auravision AVRn Avid M-JPEG Avid TechnologyBINK Bink Video RAD Game Tools BT20 Prosumer Video Conexant BTCVComposite Video Codec Conexant BW10 Broadway MPEG Data TranslationCapture/Compression CC12 YUV12 Codec Intel CDVC Canopus DV Codec CanopusCFCC DPS Perception Digital Processing Systems CGDI Camcorder VideoMicrosoft CHAM Caviara Champagne Winnov CMYK Uncompressed CMYKColorgraph CJPG WebCam JPEG Creative Labs CPLA YUV 4:2:0 Weitek CRAMMicrosoft Video 1 Microsoft CVID Cinepak Providenza &Boekelheide CWLTColor WLT DIB Microsoft CYUV Creative YUV Creative Labs CYUY ATITechnologies D261 H.261 DEC D263 H.263 DEC DIV3 DivX MPEG-4 DivX DIV4DivX MPEG-4 DivX DIV5 DivX MPEG-4 DivX DIVX DivX OpenDivX divx DivX DMB1Rainbow Runner Matrox hardware compression DMB2 Rainbow Runner Matroxhardware compression DSVD DV Codec DUCK TrueMotion S Duck Corporationdv25 DVCPRO Matrox dv50 DVCPRO50 Matrox dvsd Pinnacle Systems DVE2 DVE-2Videoconferencing InSoft Codec DVX1 DVX1000SP Video Lucent Decoder DVX2DVX2000S Video Decoder Lucent DVX3 DVX3000S Video Decoder Lucent DX50DivX MPEG-4 version 5 DivX DXTn DirectX Compressed Microsoft TextureDXTC DirectX Texture Microsoft Compression ELK0 Elsa Quick Codec ElsaEKQ0 Elsa Quick Codec Elsa ESCP Escape Eidos Technologies ETV1 eTreppidVideo Codec eTreppid Technologies ETV2 eTreppid Video Codec eTreppidTechnologies ETVC eTreppid Video Codec eTreppid Technologies FLJP FieldEncoded D-Vision Motion JPEG FRWA Forward Motion JPEG SoftLab-Nsk withalpha channel FRWD Forward Motion JPEG SoftLab-Nsk FVF1 Fractal VideoFrame Iterated Systems GLZW Motion LZW gabest@freemail.hu GPEG MotionJPEG gabest@freemail.hu GWLT Greyscale WLT DIB Microsoft H260 ITU H.26nIntel through H269 HFYU Huffman Lossless Codec HMCR Rendition MotionRendition Compensation Format HMRR Rendition Motion RenditionCompensation Format i263 ITU H.263 Intel IAN Indeo 4 Codec Intel ICLBCellB Videoconferencing InSoft Codec IGOR Power DVD IJPG Intergraph JPEGIntergraph ILVC Layered Video Intel ILVR ITU H.263+ Codec IPDV Giga AVIDV Codec I-O Data Device, Inc. IR21 Indeo 2.1 Intel IRAW IntelUncompressed UYUV Intel IV30 Indeo 3 Ligos through IV39 IV32 Indeo 3.2Ligos IV40 Indeo Interactive Ligos through IV49 IV50 Indeo InteractiveLigos JBYR Kensington JPEG JPEG Still Image Microsoft JPGL JPEG LightL261 Lead H.26 Lead Technologies L263 Lead H.263 Lead Technologies LCMWMotion CMW Codec Lead Technologies LEAD LEAD Video Codec LeadTechnologies LGRY Grayscale Image Lead Technologies Ljpg LEAD MJPEGCodec Lead Technologies LZO1 Lempel-Ziv- Markus Oberhumer OberhumerCodec M263 H.263 Microsoft M261 H.261 Microsoft M4S2 MPEG-4 (automaticMicrosoft WMP download) MC12 Motion Compensation ATI Technologies FormatMCAM Motion Compensation ATI Technologies Format MJ2C Motion JPEG 2000Morgan Multimedia mJPG Motion JPEG including IBM Huffman Tables MJPGMotion JPEG MMES MPEG-2 ES Matrox MP2A Eval download Media Excel MP2TEval download Media Excel MP2V Eval download Media Excel MP42 MPEG-4(automatic Microsoft WMP download) MP43 MPEG-4 (automatic Microsoft WMPdownload) MP4A Eval download Media Excel MP4S MPEG-4 (automaticMicrosoft WMP download) MP4T Eval download Media Excel MP4V Evaldownload Media Excel MPEG MPEG MPG4 MPEG-4 (automatic Microsoft WMPdownload) MPG4 MPEG-4 Microsoft MPGI MPEG Sigma Designs MRCA MrcodecFAST Multimedia MRLE Microsoft RLE Microsoft MSVC Microsoft Video 1Microsoft MSZH AVImszh Kenji Oshima MTX1 Matrox through MTX9 MV12 MWV1Aware Motion Wavelets Aware Inc. nAVI NTN1 Video Compression 1 NogatechNVDS NVidia Texture Format NVidia NVHS NVidia Texture Format NVidia NHVUNVidia Texture Format NVidia NVS0-NVS5 NVidia NVT0-NVT5 NVidia PDVC DVCcodec I-O Data Device, Inc. PGVV Radius Video Vision Radius PHMOPhotomotion IBM PIM1 Pegasus Imaging PIM2 Pegasus Imaging PIMJ LosslessJPEG Pegasus Imaging PIXL Video XL Pinnacle Systems PVEZ PowerEZHorizons Technology PVMM PacketVideo PacketVideo Corporation CorporationMPEG-4 PVW2 Pegasus Wavelet Pegasus Imaging Compression qpeq QPEG 1.1Q-Team QPEG QPEG Q-Team raw Raw RGB RGBT 32 bit support ComputerConcepts RLE Run Length Encoder Microsoft RLE4 4 bpp Run MicrosoftLength Encoder RLE8 8 bpp Run Microsoft Length Encoder RMP4 MPEG-4 ASSigma Designs Profile Codec RT21 Real Time Video 2.1 Intel rv20RealVideo G2 Real rv30 RealVideo 8 Real RVX RDX Intel s422 VideoCap C210Tekram YUV Codec International SAN3 DivX 3 SDCC Digital Camera Codec SunCommunications SEDG Samsung MPEG-4 Samsung SFMC Surface Fitting MethodCrystalNet SMSC Proprietary codec Radius SMSD Proprietary codec Radiussmsv Wavelet Video WorldConnect (corporate site) SP54 SunPlus SPIGSpigot Radius SQZ2 VXTreme Video Codec V2 Microsoft SV10 Video R1Sorenson Media STVA ST CMOS Imager Data ST Microelectronics STVB ST CMOSImager Data ST Microelectronics STVC ST CMOS Imager Data STMicroelectronics (Bunched) STVX ST CMOS Imager Data ST MicroelectronicsSTVY ST CMOS Imager Data ST Microelectronics SVQ1 Sorenson VideoSorenson Media TLMS Motion Intraframe Codec TeraLogic TLST MotionIntraframe Codec TeraLogic TM20 TrueMotion 2.0 Duck Corporation TM2XTrueMotion 2X Duck Corporation TMIC Motion Intraframe Codec TeraLogicTMOT TrueMotion S Horizons Technology TR20 TrueMotion RT 2.0 DuckCorporation TSCC TechSmith Screen Techsmith Corp. Capture Codec TV10Tecomac Low- Tecomac, Inc. Bit Rate Codec TVJP Pinnacle/Truevision TVMJPinnacle/Truevision TY2C Trident Decompression Trident Microsystems TY2NTrident Microsystems TY0N Trident Microsystems UCOD ClearVideoeMajix.com ULTI Ultimotion IBM Corp. V261 Lucent VX2000S Lucent V655 YUV4:2:2 Vitec Multimedia VCR1 ATI Video Codec 1 ATI Technologies VCR2 ATIVideo Codec 2 ATI Technologies VCR3-9 ATI Video Codecs ATI TechnologiesVDCT VideoMaker Pro DIB Vitec Multimedia VDOM VDOWave VDONet VDOWVDOLive VDONet VDTZ VideoTizer YUV Codec Darim Vision Co. VGPXVideoGramPix Alaris VIFP VFAPI Codec VIDS Vitec Multimedia VIVO VivoH.263 Vivo Software VIXL Video XL Pinnacle Systems VLV1 VideoLogic VP30VP3 On2 VP31 VP3 On2 vssv VSS Video Vanguard Software Solutions VX1KVX1000S Video Codec Lucent VX2K VX2000S Video Codec Lucent VXSP VX1000SPVideo Codec Lucent VYU9 ATI YUV ATI Technologies VYUY ATI YUV ATITechnologies WBVC W9960 Winbond Electronics WHAM Microsoft Video 1Microsoft WINX Winnov Software Winnov Compression WJPG Winbond JPEG WNV1Winnov Hardware Winnov Compression x263 Xirlink XVID XVID MPEG-4 XVIDXLV0 XL Video Decoder NetXL Inc. XMPG XING MPEG XING CorporationXWV0-XWV9 XiWave Video Codec XiWave XXAN Origin Y411 YUV 4:1:1 MicrosoftY41P Brooktree YUV 4:1:1 Conexant Y8 Grayscale video YC12 YUV 12 codecIntel YUV8 Caviar YUV8 Winnov YUY2 Raw, uncompressed Microsoft YUV 4:2:2YUYV Canopus ZLIB ZPEG Video Zipper Metheus ZyGo ZyGo Video ZyGo Digital

Referring again to FIG. 3, after a compression method 111 is selectedfor a scene 206, a compression module 310 compresses the scene 206 usingthe selected compression method 111 of the polymorphic codec 208. Anoutput module 312 receives the resulting compressed media signal 210and, in one embodiment, adds method identifiers 209 to indicate whichcompression method 111 was used to compress each scene 206. In otherembodiments, the method identifiers 209 may be added by the compressionmodule 310 or at other points in the compression process. The outputmodule 312 then delivers the compressed media signal 210 (with methodidentifiers 209) to the destination system 204 via the network 114.

In one embodiment, the input module 302 and the selection module 306 maybe components of the polymorphic codec 208. This would allow thepolymorphic codec 208 to appear to a video application as a standardcodec 110 with a single compression method 111, although multiplecompression methods 111 would actually be used. Many video applicationssupport plug-in codecs 110, which would allow an existing application tobe upgraded to implement the present invention by adding a plug-inpolymorphic codec 208.

Those of skill in the art will recognize that the embodiment of FIG. 3is primarily applicable to streaming media applications, such as videoconferencing. In an alternative embodiment, as depicted in FIG. 4, theoutput module 312 may be coupled to a storage device 402, such as CD orDVD recorder, flash card writer, or the like. As depicted, thecompressed media signal 210 (and method identifiers 209) may be storedon an appropriate storage medium 404, which is then physically deliveredto the destination system 204. In such an embodiment, the destinationsystem 204 would include a media reader (not shown), such as a DVD-ROMdrive, for reading the compressed media signal 210 from the storagemedium 404.

Unlike conventional media compression techniques, the original mediasignal 108 is not compressed using a single codec, such as MPEG-2 forDVDs. Rather, each scene 206 is automatically compressed using the bestcompression method 111 of a polymorphic codec 208 for that scene 206.Using the above-described technique, between 10 to 12 hours ofDVD-quality video may be stored on a single recordable DVD.

FIG. 5 illustrates additional details of the selection module 306. Asnoted above, the identification module 304 receives the original mediasignal 108 and identifies individual scenes 206, as well ascharacteristics 502 of each scene 206. The characteristics 502 mayinclude, for instance, motion characteristics, color characteristics,YUV signal characteristics, color grouping characteristics, colordithering characteristics, color shifting characteristics, lightingcharacteristics, and contrast characteristics. Those of skill in the artwill recognize that a wide variety of other characteristics of a scene206 may be identified within the scope of the invention.

Motion is composed of vectors resulting from object detection. Relevantmotion characteristics may include, for example, the number of objects,the size of the objects, the speed of the objects, and the direction ofmotion of the objects.

With respect to color, each pixel typically has a range of values forred, green, blue, and intensity. Relevant color characteristics mayinclude how the ranges of values change through the frame set, whethersome colors occur more frequently than other colors (selection), whethersome color groupings shift within the frame set, whether differencesbetween one grouping and another vary greatly across the frame set(contrast).

In one embodiment, an artificial intelligence (AI) system 504, such as aneural network or expert system, receives the characteristics 502 of thescene 206, as well as a target data rate 506 for the compressed mediasignal 210. The AI system 504 then determines whether a compressionmethod 111 of the polymorphic codec 208 has previously been found tooptimally compress a scene 206 with the given characteristics at thetarget data rate 506. As explained below, the AI system 504 may beconceptualized as “storing” associations between sets of characteristics502 and optimal compression methods 111. If an association is found, theselection module 306 outputs the compression method 111 (or anindication thereof) as the “selected” compression method 111.

In some cases, however, a scene 206 having the specified characteristicsmay not have been previously encountered. Accordingly, the selectionmodule makes a copy of the scene 206, referred to herein as a baselinesnapshot 508, which serves as a reference point for determiningcompression quality.

Thereafter, a compression module 510 tests different compression methods111 of the polymorphic codec 208 on the scene 206. In one embodiment,the compression module 510 is also the compression module 310 of FIG. 3.As depicted, the compression module 510 compresses the scene 206 usingdifferent compression methods 111 at the target data rate 506 to producemultiple compressed test scenes 512.

The compression methods 111 may be tested sequentially, at random, or inother ways, and all of the compression methods 111 need not be tested.In one embodiment, input from the AI system 504 may assist withselecting a subset of the compression methods 111 for testing. In somecases, a time limit may be imposed for testing in order to facilitatereal-time compression. Thus, when the time limit is reached, noadditional compressed test scenes 512 are generated.

In one embodiment, a comparison module 514 compares the compressionquality of each compressed test scene 512 with the baseline snapshot 508according to a set of criteria 516. The criteria 516 may be based on acomparison of Peak Signal to Noise Ratios (PSNRs), which may becalculated, for an M×N frame, by:

$\begin{matrix}{{P\; S\; N\; R} = {20 \times {\log_{10}( \frac{255}{\sqrt{\frac{1}{M \times N}{\sum\limits_{m = 0}^{M - 1}\; {\sum\limits_{n = 0}^{N - 1}\; \lbrack {{f^{\prime}( {m,n} )} - {f( {m,n} )}} \rbrack^{2}}}}} )}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

where f is the original frame and f′ is the uncompressed frame.Alternatively, Root Mean Square Error (RMSE), Signal to Noise Ratio(SNR), or other objective quality metrics may be used as known to thoseof skill in the art.

In certain embodiments, a Just Noticeable Difference (JND) image qualitymetric calculation may be used. JND is a robust objective picturequality measurement method known to those skilled in the art. Itincludes three dimensions for evaluation of dynamic and complex motionsequences-spatial analysis, temporal analysis and full color analysis.By using a model of the human visual system in a picture differencingprocess, JND produces results that are independent of the compressionprocess and resulting artifacts.

In one embodiment, the comparison module 514 automatically selects thecompression method 111 used to generate the compressed scene 512 thathas the highest compression quality when compared to the baselinesnapshot 508 according to the set of criteria 516. That compressionmethod 111 (or an indication thereof) is then output by the selectionmodule 306 as the selected compression method 111.

The comparison module 514 tells the AI system 504 which compressionmethod 111 was selected for the scene 206. This allows the AI system 504to make an association between the identified characteristics 502 of thescene 206 and the selected compression method 111. Thus, in the future,the AI system 504 may automatically select the compression method 111for a similar scene 206 without the need for retesting by the comparisonmodule 514.

Referring also to FIG. 3, in one configuration, the highest-qualitycompressed test scene 512 a is simply passed to the output module 312(not shown) to be included in the compressed media signal 210. However,the compression module 310 could recompress the scene 206 using theselected compression method 111 in certain embodiments.

In an alternative approach, the AI system 504 shown in FIG. 5 or itsequivalent is not used. Rather, the selection module 306 may always testvarious compression methods 111 on each scene 206 and select thecompression method 111 that produces the highest compression quality fora scene 206 without exceeding the target data rate 506. In such anembodiment, the identification module 304 would not need to providecharacteristics 502 of a scene 206 to the selection module 306.Moreover, the selection module 306 may simply operate on fixed-sizedsegments of the media signal 108.

FIG. 6 provides an example of the above-described processes. Supposethat the identification module 304 finds a scene 206 a having aparticular set of characteristics 502 a. In one embodiment, the AIsystem 504 searches an association 602 between the characteristics 502 aand a particular compression method 111. While the AI system 504 isdepicted as including characteristics 502, associations 602, andcompression methods 111, those skilled in the art will recognize thatthese entities may be represented by various codes, hashes, or otheridentifiers.

Assuming that no such association 602 is found, a baseline snapshot 508of the scene 206 a is taken. In addition, the compression module 510compresses the scene 206 a at the target data rate 506 using a number ofdifferent compression methods 111 a-c of the polymorphic codec 208 tocreate a plurality of compressed test scenes 512 a-c. These test scenes512 a-c are then compared against the baseline snapshot 508 according toa set of criteria 516, e.g., PSNR.

Suppose that the compressed test scene 512 a produced by one compressionmethod 111 a (“Codec 1”) results in the highest compression quality,e.g., the highest PSNR. In such a case, the comparison module 514 wouldinform the AI system 504 so that an association 602 could be madebetween the characteristics 502 a of the scene 206 a and the selectedcompression method 111 a. Thus, if a scene 206 having the samecharacteristics 502 a is encountered in the future, the AI system 504could simply identify the optimal compression method 111 a without theneed for retesting.

As further illustrated in FIG. 6, the compression module 510 mayconcurrently test multiple compression methods 111 in a multiprocessingenvironment using multiple computer processors or CPUs (centralprocessing units) 604. For example, the illustrated compression methods111 a-c (or multiple instances of the compression module 510) mayexecute within separate processing threads of a multiprocessingoperating system (OS), such as UNIX®, Windows XP®, or the like. The OSmay utilize any number of CPUs 604. In one embodiment, a separate CPU604 a-c is provided for each compression method 111 a-c to be tested atthe same time. This ensures that an optimal compression method 111 for ascene 206 may be selected in real time.

Referring to FIG. 7, the AI system 504 may be implemented using atypical feedforward neural network 700 comprising a plurality ofartificial neurons 702. A neuron 702 receives a number of inputs (eitherfrom original data, or from the output of other neurons in the neuralnetwork 700). Each input comes via a connection that has a strength (or“weight”); these weights correspond to synaptic efficacy in a biologicalneuron. Each neuron 702 also has a single threshold value. The weightedsum of the inputs is formed, and the threshold subtracted, to composethe “activation” of the neuron 702 (also known as the post-synapticpotential, or PSP, of the neuron 702). The activation signal is passedthrough an activation function (also known as a transfer function) toproduce the output of the neuron 702.

As illustrated, a typical neural network 700 has neurons 702 arranged ina distinct layered topology. The “input” layer 704 is not composed ofneurons 702, per se. These units simply serve to introduce the values ofthe input variables (i.e., the scene characteristics 502). Neurons 702in the hidden 706 and output 708 layers are each connected to all of theunits in the preceding layer.

When the network 700 is executed, the input variable values are placedin the input units, and then the hidden and output layer units areprogressively executed. Each of them calculates its activation value bytaking the weighted sum of the outputs of the units in the precedinglayer, and subtracting the threshold. The activation value is passedthrough the activation function to produce the output of the neuron 702.When the entire neural network 700 has been executed, the outputs of theoutput layer 708 act as the output of the entire network 700 (i.e., theselected compression method 111).

While a feedforward neural network 700 is depicted in FIG. 7, those ofskill in the art will recognize that other types of neural networks 700may be used, such as feedback networks, Back-Propagated Delta RuleNetworks (BP) and Radial Basis Function Networks (RBF). In otherembodiments, an entirely different type of AI system 504 may be used,such as an expert system.

In still other embodiments, the AI system 504 may be replaced by lookuptables, databases, or other data structures that are capable ofsearching for a compression method 111 based on a specified set ofcharacteristics 502. Thus, the invention should not be construed asrequiring an AI system 504.

In one embodiment, as shown in FIG. 8, the comparison module 514 mayconsider other factors in addition to (or in lieu of) compressionquality in determining which compression method 111 to automaticallyselect for a particular scene 206. For instance, the use of certaincompression methods 111 may incur licensing costs 802 based on patentsor other intellectual property rights. The licensing costs 802 may betied to the number of times the compression method 111 is used, theamount of data compressed using the compression method 111, or in otherways.

While one compression method 111 may provide an exceptionally highcompression quality (e.g., PSNR), its licensing cost 802 may exceed thevalue of the transmission and would not be cost justified. Indicationsof the licensing costs 802 for various compression methods 111 may bestored in a table or the like that is accessible to the comparisonmodule 514.

In one embodiment, the licensing costs 802 are considered only when anumber of the best compression methods 111 produce similar results,e.g., the compression qualities differ by no more than a thresholdamount. In the example of FIG. 8, the first three compression methods111 produce output of similar quality. However, the compression method111 with the highest PSNR score is more than two times more expensivethan the compression method 111 with the next highest PSNR score, whichis, itself, almost three times more expensive than the compressionmethod 111 with the third highest PSNR score. In one configuration, thecomparison module 514 would select the compression method 111 with thethird highest PSNR score due to its much lower licensing cost 802.

In other embodiments, the comparison module 514 may create a compositescore (not shown) based on the PSNR score, the licensing cost 802, andother possible factors. In still other embodiments, the comparisonmodule 514 may calculate an anticipated cost (not shown) for the entiretransmission and seek to minimize that cost over all of the codecselection decisions. Hence, the comparison module 514 might select amore expensive compression method 111 for certain scenes 206, where asubstantial increase in quality is realized, while selecting lessexpensive compression methods 111 for other scenes 206.

Referring to FIG. 10, a user of the source system 202 may specify aparticular target data rate 506, e.g., 512 kbps, for videocommunication. However, there is no guarantee that the destinationsystem 204 may be able to process data that quickly. Moreover, there isno guarantee that the network 114 will always provide the same amount ofbandwidth. As a result, there may be a need to periodically change thetarget data rate 506 within the selection module 306 of the sourcesystem 202, since the target data rate 506 will affect which compressionmethods 111 are selected for various scenes 206.

For example, the destination system 204 may be embodied as avideo-enabled cellular telephone. Typically, the bandwidth over cellularnetworks 114 is limited. Similarly, the processing power of a cellulartelephone is substantially less than that of a personal computer ordedicated video conferencing system.

Thus, although the user of the source system 202 specifies a target datarate 506 of 512 kbps, the destination system 204 and/or network 114 maynot be up to the challenge. In one embodiment, in response to receivinga connection request, the destination system 204 provides the sourcesystem 202 with a modified target data rate 902, e.g., 128 kpbs. Themodified rate 902 may be communicated to the source system 202 using anystandard data structure or technique. Thereafter, depending on theconfiguration, the target data rate 506 may be replaced by the modifiedrate 902.

In certain embodiments, an actual data rate is not communicated. Rather,a message is sent specifying one or more constraints or capabilities ofthe destination system 204 or network 114, in which case it would be upto the source system 202 to revise the target data rate 506 asappropriate. A technique of altering the target data rate 506 inresponse to various conditions is referred to herein as “dynamicstreaming.”

In one embodiment, dynamic streaming may be employed where no specificmessage is sent by destination system 204. The source system 202 may uselatency calculations, requests to resend lost packets, etc., todynamically determine the target data rate 506 for purposes of selectinga compression method 111.

Referring to FIG. 10, video frames 1002 within a scene 206 may besubdivided into a plurality of sub-frames 1004. While the depicted videoframe 1002 is subdivided into four sub-frames 1004 a-d of equal size,the invention is not limited in this respect. For instance, a videoframe 1002 may be subdivided into any number of sub-frames 1004,although too many sub-frames 1004 may adversely affect compressionquality. Moreover, the sub-frames 1004 need not be of equal size. Forexample, sub-frames 1004 near the center of the video frame 1002 may besmaller due to the relatively greater amount of motion in this area.

In certain embodiments, the sub-frames 1004 may be defined by objectsrepresented within the video frame 1002. As an example, the head of aperson could be defined as a separate object and, hence, a differentsub-frame 1004 from the background. Algorithms (e.g., MPEG-4) forobjectifying a scene within a video frame 1002 are known in the art.

A set of sub-frames 1004 a-d within a scene 206 exhibit characteristics502 a-d, and may be treated, for practical purposes, like a completevideo frame 1002. Accordingly, using the techniques described above, thecharacteristics 502 a-d may be used to determine an optimal compressionmethod 111 a-d for the compressing the respective sub-frames 1004 a-d.For example, an AI system 504 (not shown) may be used to determinewhether an association 602 exists between a set of characteristics 502and a particular compression method 111. If no association 602 exists,compression 510 and comparison 514 modules (not shown) may be used totest a plurality of compression methods 111 on the respective sub-frames1004 to determine the optimal compression method 111.

Thus, different sub-frames 1004 a-d of a single scene 206 may becompressed using different compression methods 111 a-d. In theillustrated embodiment, four different compression methods 111 a-d areused.

While specific embodiments and applications of the present inventionhave been illustrated and described, it is to be understood that theinvention is not limited to the precise configuration and componentsdisclosed herein. Various modifications, changes, and variationsapparent to those of skill in the art may be made in the arrangement,operation, and details of the methods and systems of the presentinvention disclosed herein without departing from the spirit and scopeof the present invention.

1. A media compression method comprising: obtaining a media signal to becommunicated to a destination system; identifying a plurality ofsegments within the media signal; compressing the plurality of segmentswith a codec supporting multiple compression methods, wherein the codecautomatically selects different compression methods to respectivelycompress at least two of the segments, wherein the compression methodsare automatically selected to produce a highest compression quality forthe respective segments according to a set of criteria without exceedinga target data rate; and delivering the compressed segments to thedestination system with an indication of which compression method wasused by the codec to compress each segment.
 2. The method of claim 1,wherein the compression methods are selected from the group consistingof discrete cosine transform (DCT), fractal, and wavelet compressionmethods.
 3. The method of claim 1, wherein a firstautomatically-selected compression method comprises a discrete cosinetransform (DCT) method and a second automatically-selected compressionmethod comprises a fractal method.
 4. The method of claim 1, wherein afirst automatically-selected compression method comprises a discretecosine transform (DCT) method and a second automatically-selectedcompression method comprises a wavelet method.
 5. The method of claim 1,wherein automatically selecting further comprises: identifying aplurality of characteristics of a segment; and searching for acompression method that is associated with the identifiedcharacteristics of the segment.
 6. The method of claim 5, wherein thecharacteristics are selected from the group consisting of motioncharacteristics and color characteristics.
 7. The method of claim 6,wherein searching further comprises using an Artificial Intelligence(AI) system to locate a compression method associated with theidentified characteristics of a segment.
 8. The method of claim 7,wherein the AI system comprises a neural network.
 9. The method of claim7, wherein the AI system comprises an expert system.
 10. The method ofclaim 1, wherein automatically selecting further comprises: testing atleast a subset of the compression methods on a segment; andautomatically selected the compression method that produces a highestcompression quality for the segment according to a set of criteriawithout exceeding the target data rate.
 11. The method of claim 10,wherein testing further comprises: storing a baseline snapshot of thesegment; and for each compression method to be tested: compressing thesegment at or below the target data rate using one of the compressionmethods in the library; decompressing the segment using a correspondingdecompression method; and comparing the quality of the decompressedsegment with the baseline snapshot according to the set of criteria. 12.The method of claim 11, wherein comparing further comprises comparingthe quality according to a Peak Signal to Noise Ratio (PSNR).
 13. Themethod of claim 11, wherein comparing further comprises comparing thequality according to a Just Noticeable Difference (JND) value.
 14. Themethod of claim 11, further comprising: identifying a plurality ofcharacteristics of a segment; and associating the identifiedcharacteristics of the segment with the automatically-selectedcompression method.
 15. The method of claim 10, wherein testing at leasta subset of the compression methods comprises testing a plurality ofcompressions methods concurrently using a plurality of separateprocessors.
 16. The method of claim 15, wherein the number of processorsat least equals the number of compression methods to be tested.
 17. Themethod of claim 10, wherein each compression method is tested within aseparate processing thread of a multiprocessing operating system. 18.The method of claim 5, wherein searching further comprises searching foran association between the identified characteristics and a set ofparameters to be used with the automatically-selected compressionmethod; wherein compressing further comprises compressing the segmentusing the automatically-selected compression method with the associatedset of parameters; and wherein delivering further comprises deliveringthe compressed segment to the destination system with an indication ofwhich compression method and which set of parameters were used tocompress the segment.
 19. The method of claim 10, wherein testingfurther comprises testing compression methods on the segment usingdifferent sets of parameters and automatically selecting the compressionmethod and set of parameters that produce a highest compression qualityfor the segment according to a set of criteria without exceeding thetarget data rate; wherein compressing further comprises compressing thesegment using the automatically-selected compression method with theautomatically-selected parameters; and wherein delivering furthercomprises delivering the compressed segment to the destination systemwith an indication of which compression method and set of parameterswere used to compress the segment.
 20. The method of claim 19, furthercomprising: identifying a plurality of characteristics of a segment; andassociating the automatically-selected compression method and theautomatically-selected set of parameters with the identifiedcharacteristics of the segment.
 21. The method of claim 1, whereinidentifying further comprises detecting a segment change in response toone frame of the media signal being sufficiently different from aprevious frame.
 22. The method of claim 1, wherein identifying furthercomprises detecting a segment change in response to the passage of afixed period of time.
 23. The method of claim 1, wherein deliveringfurther comprises streaming each compressed segment to the destinationsystem through a network.
 24. The method of claim 1, wherein deliveringfurther comprises storing each compressed segment on a storage medium.25. The method of claim 1, wherein at least one compression method hasan associated licensing cost, and wherein selecting further comprisesautomatically selecting the compression method having the leastlicensing cost in response to two or more compression methods producingsubstantially the same quality of compressed output for a segment.
 26. Amedia compression method comprising: obtaining a media signal to becommunicated to a destination system; automatically selecting differentcompression methods to respectively compress at least two of thesegments of the media signal, wherein the compression methods areautomatically selected to produce a highest compression quality for therespective segments without exceeding a target data rate; compressingthe segments using the automatically-selected compression methods; anddelivering the compressed segments to the destination system with anindication of which compression method was used to compress eachsegment.
 27. A media compression method comprising: providing a libraryof compression methods, at least one compression method having anassociated licensing cost; obtaining a media signal to be communicatedto a destination system; identifying a plurality of segments within themedia signal; automatically selecting different compression methods fromthe library to respectively compress at least two of the segments,wherein the compression methods are automatically selected to produce ahighest compression quality at the lowest licensing cost for therespective segments according to a set of criteria without exceeding atarget data rate; compressing the segments using theautomatically-selected compression methods; and delivering thecompressed segments to the destination system with an indication ofwhich compression method was used to compress each segment.
 28. A methodfor communicating a media signal comprising: selectively compressing atleast two segments of a media signal using different compression methodsavailable within a single codec, wherein the compression methods areautomatically selected to produce a highest compression quality for therespective segments according to a set of criteria without exceeding atarget data rate; and delivering each compressed segment to adestination system with an indication of which compression method wasused by the codec to compress each segment.
 29. A method comprising:receiving a media signal comprising a first segment compressed using afirst compression method of a codec and a second segment compressedusing a second compression method of the same codec, wherein the firstand second codecs are automatically selected based on which compressionmethod produces a highest compression quality for each segment accordingto a set of criteria without exceeding a target data rate; receiving anindication of which compression method was used to compress eachsegment; decompressing the first segment using the first indicatedcompression method; and decompressing the second segment using thesecond indicated compression method.
 30. The method of claim 29, furthercomprising presenting the first and second decompressed segments to auser.
 31. A media compression system comprising: an input module toobtain a media signal to be communicated to a destination system; anidentification module to identify a plurality of segments within themedia signal; a codec to automatically select different compressionmethods to respectively compress at least two of the segments, whereinthe compression methods are automatically selected to produce a highestcompression quality for the respective segments according to a set ofcriteria without exceeding a target data rate, and wherein the codec isto compress the segments using the automatically-selected compressionmethods; and an output module to deliver the compressed segments to thedestination system with an indication of which compression method wasused to compress each segment.
 32. The system of claim 31, wherein thecompression methods are automatically selected from the group consistingof discrete cosine transform (DCT), fractal, and wavelet compressionmethods.
 33. The system of claim 31, wherein a firstautomatically-selected compression method comprises a discrete cosinetransform (DCT) method and a second automatically-selected compressionmethod comprises a fractal method.
 34. The system of claim 31, wherein afirst automatically-selected compression method comprises a discretecosine transform (DCT) method and a second automatically-selectedcompression method comprises a wavelet method.
 35. The system of claim31, wherein the identification module is to identify a plurality ofcharacteristics of a segment; and wherein the codec is to search for acompression method that is associated with the identifiedcharacteristics of the segment.
 36. The system of claim 35, wherein thecharacteristics are selected from the group consisting of motioncharacteristics and color characteristics.
 37. The system of claim 36,wherein the codec comprises an Artificial Intelligence (AI) system tolocate a compression method associated with the identifiedcharacteristics of a segment.
 38. The system of claim 37, wherein the AIsystem comprises a neural network.
 39. The system of claim 37, whereinthe AI system comprises an expert system.
 40. The system of claim 31,wherein the codec is to test at least a subset of the compressionmethods on a segment and automatically select the compression methodthat produces a highest compression quality for the segment according toa set of criteria without exceeding the target data rate.
 41. The systemof claim 40, wherein the codec is to store a baseline snapshot of thesegment and, for each compression method to be tested, have the segmentcompressed at or below the target data rate using one of the compressionmethods in the library, have the segment decompressed using acorresponding decompression method, and compare the quality of thedecompressed segment with the baseline snapshot according to the set ofcriteria.
 42. The system of claim 41, wherein the codec is to comparethe quality according to a Peak Signal to Noise Ratio (PSNR).
 43. Thesystem of claim 41, wherein the codec is to compare the qualityaccording to a Just Noticeable Difference (JND) value.
 44. The system ofclaim 41, wherein the identification module is to identify a pluralityof characteristics of a segment; and wherein the codec is to associatethe identified characteristics of the segment with theautomatically-selected compression method.
 45. The system of claim 40,wherein the codec is to test at least a subset of the compressionmethods concurrently using a plurality of separate processors.
 46. Thesystem of claim 45, wherein the number of processors at least equals thenumber of compression methods to be tested.
 47. The system of claim 40,wherein the codec is to test each compression method within a separateprocessing thread of a multiprocessing operating system.
 48. The systemof claim 35, wherein the codec is to search for an association betweenthe identified characteristics and a set of parameters to be used withthe automatically-selected compression method; wherein the compressionmodule is to compress the segment using the automatically-selectedcompression method with the associated set of parameters; and whereinthe output module is to deliver the compressed segment to thedestination system with an indication of which compression method andwhich set of parameters were used to compress the segment.
 49. Thesystem of claim 40, wherein the codec is to test the compression methodson the segment using different sets of parameters and automaticallyselect the compression method and set of parameters that produce ahighest compression quality for the segment according to a set ofcriteria without exceeding the target data rate; wherein the compressionmodule is to compress the segment using the automatically-selectedcompression method with the automatically-selected parameters; andwherein the output module is to deliver the compressed segment to thedestination system with an indication of which compression method andset of parameters were used to compress the segment.
 50. The system ofclaim 49, wherein the identification module is to identify a pluralityof characteristics of a segment; and wherein the codec is to associatethe automatically-selected compression method and theautomatically-selected set of parameters with the identifiedcharacteristics of the segment.
 51. The system of claim 31, wherein theidentification module is to detect a segment change in response to oneframe of the media signal being sufficiently different from a previousframe.
 52. The system of claim 31, wherein the identification module isto detect a segment change in response to the passage of a fixed periodof time.
 53. The system of claim 31, wherein the output module is tostream each compressed segment to the destination system through anetwork.
 54. The system of claim 31, wherein the output module is tostore each compressed segment on a storage medium.
 55. The system ofclaim 31, wherein at least one compression method has an associatedlicensing cost, and wherein the codec is to automatically select thecompression method having the least licensing cost in response to two ormore compression methods producing substantially the same quality ofcompressed output for a segment.